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Related Experiment Video

Updated: Jan 26, 2026

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umx: Twin and Path-Based Structural Equation Modeling in R.

Timothy C Bates1, Hermine Maes2, Michael C Neale2

  • 1Department of Psychology,University of Edinburgh,Edinburgh,UK.

Twin Research and Human Genetics : the Official Journal of the International Society for Twin Studies
|April 5, 2019
PubMed
Summary
This summary is machine-generated.

The umx package provides accessible structural equation modeling (SEM) tools for social and behavior genetics research. It simplifies model development, modification, and comparison, offering robust support for twin models and gene-environment interactions.

Keywords:
OpenMxRpath modelsstructural equation modelingtwin models

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Area of Science:

  • Quantitative psychology
  • Behavior genetics
  • Statistical modeling

Background:

  • Structural equation modeling (SEM) is crucial in social sciences and behavior genetics.
  • Existing SEM tools can be complex for users in these fields.
  • A need exists for accessible and user-friendly SEM software.

Purpose of the Study:

  • To introduce umx, a new software package for structural equation modeling.
  • To provide concise syntax and helpful defaults for social science and behavior genetics researchers.
  • To facilitate model development, modification, comparison, and output generation.

Main Methods:

  • Development of the umx package in R.
  • Implementation of functions for standard multigroup twin models.
  • Support for raw and covariance data, including joint ordinal data.
  • Inclusion of ACE models, covariate analysis, common- and independent-pathway models, and gene × environment interaction models.

Main Results:

  • umx offers immediate access to SEM functionalities.
  • The package features concise syntax and user-friendly defaults.
  • It supports various behavior genetic models, including complex interaction models.
  • Graphical and tabular outputs are available for model interpretation.

Conclusions:

  • umx enhances the accessibility of SEM for researchers in social sciences and behavior genetics.
  • The package streamlines the analysis of complex genetic and environmental models.
  • Associated resources like a tutorial site and forum support user learning and engagement.